Recall and Recognition in an Attractor Neural Network Model of Memory Retrieval

This paper presents an attractor neural network (ANN) model of recall and recognition. It is shown that an ANN Hopfield-based network can qualitatively account for a wide range of experimental psychological data pertaining to these two main aspects of memory retrieval. After providing simple, straightforward definitions of recall and recognition in the model, a wide variety of ‘high-level’ psychological phenomena are shown to emerge from the ‘low-level’ neural-like properties of the network. It is shown that modeling the effect of memory load on the network's retrieval properties requires the incorporation of noise into the network's dynamics. External projections may account for phenomena related with the stored items’ associative links, but are not sufficient for representing context. With low memory load, the network generates retrieval response times which have the same distribution form as that observed experimentally. Finally, estimations of the probabilities of successful recall and recognition are obtained, possibly enabling further quantitative examination of the model